Dataset Collection. In the absence of clinically-aligned user data [1]}, we source disorder terms from the DSM-5-TRSee sec:limitations for a discussion on method caveats and standards of diagnosis in clinical psychology. [2]} and utilize regular expressions to identify disclosures of self-reported statements of diagnosis [3]}, [4]}, [5]} for mental health disorders in music comments.
Two Chinese native speakers
further manually filter for genuine statements of disclosure
(i.e., excluding jokes, quotes, and clearly disingenuous statements),
resulting in 1133 users with self-reported mental health disorders.
We find that, out of all disclosers, most disclose depression (81.2%), anxiety (19.9%), and bipolar (18.5%) disorders; additionally, most users (60.6%) self-identify as women, consistent with constituent gender differences of affective disorders in national studies [6]}.
Disclosers show greater platform usage (Kolmogorov-Smirnov on user levels, p<0.01),
insomnia-aligned diurnal user activity consistent with disorder symptoms [7]}, [8]},
increased engagement with playlists of sadder natures, e.g., as shown in Figure REF , loneliness (+302%), sadness (+158%), and night (+50.1%),
and decreased engagement with playlists of more active natures, e.g., exercise (-51.7%),
compared to typical users. These observations mirror affective disorder activity trends [9]} and suggest that people with affective disorders are more likely to use music reflective of negative emotions than positive emotions to manage feelings of sadness and depression [10]}.
A detailed breakdown of our data, comorbidities, and our specific regular expressions are described in Appendix Section .
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